Engelsk som Lingua Franca og Engelsk som fremmedspråk paradigmet.

Bachelor Thesis. English lector – Class of 2013.English: The purpose of the thesis is to answer and discuss if ELF and EFL are different by highlighting pragmatics in a debate between Michael Swan and Henry, G. Widdowson. Although it is a straight forward question about whether they are the same or different, does the paradigm also contain other important subjects. The main focus for this thesis has been on the notion of mistakes within native speaker and non-native speaker norms and communities, but other subjects have also been discussed because of their pragmatic role to the ELF vs. EFL paradigm. English language teaching, language status, learning and using English and Global English models are debated either positively or negatively by linguists, teachers, professors and experts in the respectable fields. This has been done be reviewing literature and sources from Barbara Seidlhofer, Jennifer Jenkins, Michael Swan, Henry G. Widdowson, debates, symposiums and online articles. The conclusion of this thesis is that ELF and EFL are different and should not be viewed as the same, but as ambassadors of English and Global English that work in their own way. There are many subjects that need further research in order to draw further conclusions, and older established ways of viewing Global English may need further update in order to be relevant for this debate.Norwegian: Målet for denne bacheloren har vært å svare og diskutere om ELF og EFL betyr det samme ved å belyse pragmatiske objekter i debatten mellom Michael Swan og Henry G. Widdowson. Selv om det stilles et ja nei spørsmål om de er det samme eller ulike, består paradigmet også av andre viktige temaer. Hovedfokuset for denne bacheloren har vært på tanken om feil i morsmål og ikke-morsmål normer og lokalsamfunn i Engelsk, men andre temaer har også blitt diskutert på grunn av sin pragmatiske rolle i henhold til ELF vs. EFL paradigmet. Engelsk språklæring, språk status, læring og bruk av Engelsk og Global Engelske modeller er debattert enten positivt eller negativt av lingvister, lærere, professorer og eksperter i deres respektable felt. Dette har blitt gjort ved å gjennomgå litteratur og kilder fra Barbara Seidlhofer, Jennifer Jenkins, Michael Swan, Henry G. Widdowson, debatter, symposier og elektroniske artikler. Konklusjonen i denne avhandlingen er at ELF og EFL er forskjellige og bør ikke bli sett på som like, men som ambassadører for Engelsk og Global Engelsk som fungerer på sin måte. Det er mange temaer som trenger videre forskning for å kunne trekke flere konklusjoner, og eldre etablerte måter å vise Global Engelsk på kan trenge ytterligere oppdatering for å være relevant for denne debatten

Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms

Meningiomas are the most common type of primary brain tumor, accounting for approximately 30% of all brain tumors. A substantial number of these tumors are never surgically removed but rather monitored over time. Automatic and precise meningioma segmentation is therefore beneficial to enable reliable growth estimation and patient-specific treatment planning. In this study, we propose the inclusion of attention mechanisms over a U-Net architecture: (i) Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a 3D MRI volume as input. Attention has the potential to leverage the global context and identify features' relationships across the entire volume. To limit spatial resolution degradation and loss of detail inherent to encoder-decoder architectures, we studied the impact of multi-scale input and deep supervision components. The proposed architectures are trainable end-to-end and each concept can be seamlessly disabled for ablation studies. The validation studies were performed using a 5-fold cross validation over 600 T1-weighted MRI volumes from St. Olavs University Hospital, Trondheim, Norway. For the best performing architecture, an average Dice score of 81.6% was reached for an F1-score of 95.6%. With an almost perfect precision of 98%, meningiomas smaller than 3ml were occasionally missed hence reaching an overall recall of 93%. Leveraging global context from a 3D MRI volume provided the best performances, even if the native volume resolution could not be processed directly. Overall, near-perfect detection was achieved for meningiomas larger than 3ml which is relevant for clinical use. In the future, the use of multi-scale designs and refinement networks should be further investigated to improve the performance. A larger number of cases with meningiomas below 3ml might also be needed to improve the performance for the smallest tumors.Comment: 16 pages, 5 figures, 3 tables. Submitted to Artificial Intelligence in Medicin

Understanding Socialism from the Outside and from the Inside: an Interview with Alberto Chilosi

Alberto Chilosi belongs to the last generation of scholars who studied the socialist system and have been able to gain first-hand experience of its operation under “real socialism”. His extraordinary testimony features a series of analyses, thoughts, and anecdotes on the workings of this system that have often been overlooked in the literature of comparative economics and in the history of economic thought, but which will undoubtedly represent an indispensable source for historians of the future. This text also offers thought-provoking materials for those who set out to think about a model of society that goes beyond the capitalist economy

Kvinners opplevelse av angst og stress i forkant av operasjon

En kvalitativ studie om hvilken informasjon om operasjonsavdelingen som pasienter som skal gjennomføre et gynekologisk kirurgisk inngrep trenger for å redusere pre-operativ angst

Kvinners opplevelse av angst og stress i forkant av operasjon

En kvalitativ studie om hvilken informasjon om operasjonsavdelingen, som pasienter som skal gjennomføre et gynekologisk kirurgisk inngrep trenger for å redusere pre-operativ angst

Teacher-student approach for lung tumor segmentation from mixed-supervised datasets

Purpose: Cancer is among the leading causes of death in the developed world, and lung cancer is the most lethal type. Early detection is crucial for better prognosis, but can be resource intensive to achieve. Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain. Methods: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework consists of two models: the student that performs end-to-end automatic tumor segmentation and the teacher that supplies the student additional pseudo-annotated data during training. Results: Using only a small proportion of semantically labeled data and a large number of bounding box annotated data, we achieved competitive performance using a teacher-student design. Models trained on larger amounts of semantic annotations did not perform better than those trained on teacher-annotated data. Our model trained on a small number of semantically labeled data achieved a mean dice similarity coefficient of 71.0 on the MSD Lung dataset. Conclusions: Our results demonstrate the potential of utilizing teacher-student designs to reduce the annotation load, as less supervised annotation schemes may be performed, without any real degradation in segmentation accuracy.publishedVersio

Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding

As lung cancer evolves, the presence of potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. A method for accurate and automatic segmentation is hence decisive for quantitatively describing lymph nodes. In this study, the use of 3D convolutional neural networks, either through slab-wise schemes or the leveraging of downsampled entire volumes, is investigated. As lymph nodes have similar attenuation values to nearby anatomical structures, we use the knowledge of other organs as prior information to guide the segmentation. To assess the performances, a 5-fold cross-validation strategy was followed over a dataset of 120 contrast-enhanced CT volumes. For the 1178 lymph nodes with a short-axis diameter ≥10 mm, our best-performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5 and a segmentation overlap of 80.5%. Fusing a slab-wise and a full volume approach within an ensemble scheme generated the best performances. The anatomical priors guiding strategy is promising, yet a larger set than four organs appears needed to generate an optimal benefit. A larger dataset is also mandatory given the wide range of expressions a lymph node can exhibit (i.e. shape, location and attenuation).publishedVersio

O CICLO DA HORTELÃ NA MICRORREGIÃO DE TOLEDO 1959 - 1980

This article analyzes the Mint cycle in microregion of Toledo / PR, the beginning of the 1960s until the early 1980s. Through a survey of production and trade data, the available literature and the testimony of producers, will be analyzed social and economic relations that were established around this activity. Despite the focus of the research focus on microregion of Toledo / PR, the article draws on regional character data, state, national and international, since it is not possible to understand the socio-cultural and economic relations sites without inserting them in a larger context.Este artigo analisa o ciclo da hortelã na microrregião de Toledo/PR, do início da década de 1960 até o início da década de 1980. Por meio de um levantamento dos dados de produção e comércio, da literatura disponível e do testemunho de produtores, serão analisadas as relações sociais e econômicas que foram estabelecidas em volta desta atividade. Apesar do foco da pesquisa se centrar na microrregião de Toledo/PR, o artigo se vale de dados de caráter regional, estadual, nacional e internacional, uma vez que não é possível entender as relações socioculturais e econômicas locais sem inseri-las num contexto amplo

Iterative model predictions for wildlife populations impacted by rapid climate change

To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005–2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach